Influence of Spatial Variability on Slope Reliability Using 2-D Random Fields

TLDR

The study examines slope failure probability using both traditional and advanced probabilistic methods. They compare the random finite‑element method—elastoplastic finite‑element modeling with random field theory in a Monte‑Carlo framework—to the first‑order reliability method, which estimates failure probability from a reliability index, and conduct parametric studies on slope inclination, factor of safety, and cross‑correlation of strength parameters. Simplified probabilistic analyses that ignore spatial variability can underestimate failure probability when the coefficient of variation of shear strength exceeds a critical value, and the study identifies when such approaches are inadequate and more advanced methods are needed.

Abstract

The paper investigates the probability of failure of slopes using both traditional and more advanced probabilistic analysis tools. The advanced method, called the random finite-element method, uses elastoplasticity in a finite-element model combined with random field theory in a Monte-Carlo framework. The traditional method, called the first-order reliability method, computes a reliability index which is the shortest distance (in units of directional equivalent standard deviations) from the equivalent mean-value point to the limit state surface and estimates the probability of failure from the reliability index. Numerical results show that simplified probabilistic analyses in which spatial variability of soil properties is not properly accounted for, can lead to unconservative estimates of the probability of failure if the coefficient of variation of the shear strength parameters exceeds a critical value. The influences of slope inclination, factor of safety (based on mean strength values), and cross correlation between strength parameters on this critical value have been investigated by parametric studies in this paper. The results indicate when probabilistic approaches, which do not model spatial variation, may lead to unconservative estimates of slope failure probability and when more advanced probabilistic methods are warranted.